Spatial Fairness

Spatial fairness in machine learning addresses algorithmic bias stemming from the use of location data, aiming to ensure equitable outcomes regardless of geographic location. Current research focuses on developing methods to detect and mitigate this bias, employing techniques like meta-learning to adjust model training and statistical hypothesis testing to assess fairness, as well as proposing novel spatial indexing algorithms to improve fairness in decision-making processes. This field is crucial for ensuring fairness in applications ranging from resource allocation to risk assessment, impacting both the development of responsible AI and the equitable distribution of services and opportunities.